Fairness Improvement

Fairness improvement in machine learning aims to mitigate biases in algorithms that disproportionately affect certain demographic groups, ensuring equitable outcomes across different populations. Current research focuses on developing and applying techniques like adversarial debiasing, post-processing methods (e.g., threshold optimization), and contrastive learning, often within specific model architectures such as BERT and various GNNs. These efforts are crucial for building trustworthy AI systems across diverse applications, from healthcare and finance to legal text processing and image recognition, by reducing discriminatory outcomes and promoting equitable access to AI-driven services.